To utilize Foundation Vision Language Models (VLMs) for robotic tasks and motion planning, the community has proposed different methods for injecting action components into VLMs and building the Vision-Language-Action models (VLAs). In this work, we disclose the key factors that significantly influence the performance of VLA on robot manipulation problems and focus on answering three essential design choices: which backbone to select, how to formulate the VLA architectures, and when to add cross-embodiment data. The obtained results convince us firmly to explain why we prefer VLA and develop a new family of VLAs, RoboVLMs, which require very few manual designs and achieve a new state-of-the-art performance in three simulation tasks and real-world experiments. Through our extensive experiments, which include over 8 VLM backbones, 4 policy architectures, and over 600 distinct designed experiments, we provide a detailed guidebook for the future design of VLAs. In addition to the study, the highly flexible RoboVLMs framework, which supports easy integrations of new VLMs and free combinations of various design choices, is made public to facilitate future research. We open-source all details, including codes, models, datasets, and toolkits, along with detailed training and evaluation recipes at: robovlms.github.io.

翻译：为将基础视觉语言模型（VLMs）应用于机器人任务与运动规划，学界已提出多种方法以向VLMs注入动作组件，从而构建视觉-语言-动作模型（VLAs）。本研究揭示了显著影响VLA在机器人操作问题上性能的关键因素，并聚焦于回答三个核心设计选择：应选择何种骨干网络、如何构建VLA架构，以及何时引入跨具身数据。所得结果使我们确信地阐释了为何选择VLA，并由此开发了新型VLA系列——RoboVLMs。该系列模型仅需极少人工设计，即在三项仿真任务与真实世界实验中实现了新的最先进性能。通过涵盖超过8种VLM骨干网络、4种策略架构及600余项独立设计的广泛实验，我们为未来VLA设计提供了详尽的指导手册。除研究本身外，具备高度灵活性的RoboVLMs框架（支持新VLM的便捷集成与多种设计选择的自由组合）已公开以促进后续研究。我们在robovlms.github.io开源了所有细节，包括代码、模型、数据集、工具包以及详细的训练与评估方案。